class_docstr: 🤗 Constructor for the image classifier trainer
class_docstr_tok: ["\ud83e\udd17","Constructor","for","the","image","classifier","trainer"]
code: def evaluate(self):
      all_preds = []
      all_target = []
  
      # For each image
      for image, label in tqdm(self.test):
          # Compute
          inputs = self.feature_extractor(
              images=image, return_tensors="pt"
          ).to(self.device)
          outputs = self.model(**inputs)
  
          # Get predictions from the softmax layer
          preds = outputs.logits.softmax(1).argmax(1).tolist()
          all_preds.extend(preds)
  
          # Get hypothesis
          all_target.append(label)
  
      return all_target, all_preds
  
file_path: HugsVision/build/lib/hugsvision/nnet/VisionClassifierTrainer.py
imports: import os
  from datetime import datetime
  import torch
  from sklearn import metrics
  from sklearn.metrics import precision_recall_fscore_support
  from transformers import Trainer
  from transformers.training_args import TrainingArguments
  from transformers.feature_extraction_utils import FeatureExtractionMixin
  from tqdm import tqdm
  from PIL import Image
  from hugsvision.dataio.ImageClassificationCollator import ImageClassificationCollator
label_desc: Use image node_id 5 for calling the VisionClassifierTrainer obj's underlying member method code with example usage: obj.evaluate() and returns: all_target,  all_preds